Tabla de Contenidos: “…Practice Questions -- Further Questions -- Chapter 18: Binomial Trees in Practice -- 18.1 The Binomial Model for a Non-Dividend-Paying Stock -- 18.2 Using the Binomial Tree for Options on Indices, Currencies, and Futures Contracts -- 18.3 The Binomial Model for a Dividend-Paying Stock -- 18.4 Extensions of the Basic Tree Approach -- 18.5 Alternative Procedure for Constructing Trees -- 18.6 Monte Carlo Simulation -- Summary -- Further Reading -- Quiz -- Practice Questions -- Further Questions -- Chapter 19: Volatility Smiles -- 19.1 Foreign Currency Options -- 19.2 Equity Options -- 19.3 The Volatility Term Structure and Volatility Surfaces -- 19.4 When a Single Large Jump Is Anticipated -- Summary -- Further Reading -- Quiz -- Practice Questions -- Further Questions -- Appendix: Why the Put Volatility Smile Is the Same As the Call Volatility Smile -- Chapter 20: Value at Risk and Expected Shortfall -- 20.1 The VaR and ES Measures -- 20.2 Historical Simulation -- 20.3 Model-Building Approach -- 20.4 Generalization of Linear Model -- 20.5 Quadratic Model -- 20.6 Estimating Volatilities and Correlations -- 20.7 Comparison of Approaches -- 20.8 Back Testing -- Summary -- Further Reading -- Quiz -- Practice Questions -- Further Questions -- Chapter 21: Interest Rate Options -- 21.1 Exchange-Traded Interest Rate Options -- 21.2 Embedded Bond Options -- 21.3 Black's Model -- 21.4 European Bond Options -- 21.5 Interest Rate Caps -- 21.6 European Swap Options -- 21.7 Term Structure Models -- Summary -- Further Reading -- Quiz -- Practice Questions -- Further Questions -- Chapter 22: Exotic Options and Other Nonstandard Products -- 22.1 Exotic Options -- 22.2 Agency Mortgage-Backed Securities -- 22.3 Nonstandard Swaps -- Summary -- Further Reading -- Quiz -- Practice Questions -- Further Questions -- Chapter 23: Credit Derivatives -- 23.1 Credit Default Swaps…”

“…A l'occasion du centenaire de sa disparition, un colloque scientifique sans précédent s'est réuni, sous le patronage de S.A.S. le prince de Monaco et de S.A.I. le prince Napoléon, dans le cadre prestigieux de l'opéra de Monte-Carlo puis du lycée Henri-IV à Paris, afin de faire le point sur cette souveraine longtemps calomniée et souvent méconnue. …”

Tabla de Contenidos: “…Conjugate gradient method -- Extension to general (non-quadratic) functions -- Powell's conjugate direction method -- Quasi-Newton Methods -- Closure -- Exercises -- Chapter 24: Constrained Optimization -- Constraints -- Optimality Criteria -- Sensitivity -- Duality* -- Structure of Methods: An Overview* -- Penalty methods -- Primal methods -- Dual methods -- Lagrange methods -- Exercises -- Chapter 25: Linear and Quadratic Programming Problems* -- Linear Programming -- The simplex method -- General perspective -- Quadratic Programming -- Active set method -- Linear complementary problem -- A trust region method -- Duality -- Exercises -- Chapter 26: Interpolation and Approximation -- Polynomial Interpolation -- Lagrange interpolation -- Newton interpolation -- Limitations of single-polynomial interpolation -- Hermite interpolation -- Piecewise Polynomial Interpolation -- Piecewise cubic interpolation -- Spline interpolation -- Interpolation of Multivariate Functions -- Piecewise bilinear interpolation -- Piecewise bicubic interpolation -- A Note on Approximation of Functions -- Modelling of Curves and Surfaces* -- Exercises -- Chapter 27: Basic Methods of Numerical Integration -- Newton-Cotes Integration Formulae -- Mid-point rule -- Trapezoidal rule -- Simpson's rules -- Richardson Extrapolation and Romberg Integration -- Further Issues -- Exercises -- Chapter 28: Advanced Topics in Numerical Integration* -- Gaussian Quadrature -- Gauss-Legendre quadrature -- Weight functions in Gaussian quadrature -- Multiple Integrals -- Double integral on rectangular domain -- Monte Carlo integration -- Exercises -- Chapter 29: Numerical Solution of Ordinary Differential Equations -- Single-Step Methods -- Euler's method -- Improved Euler's method or Heun's method -- Runge-Kutta methods -- Practical Implementation of Single-Step Methods…”

Tabla de Contenidos: “…-- Understanding what AI means and what's in it in an intuitive way -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Practical reinforcement learning -- Agent -- Rewards -- Environment -- State -- Model -- Value function -- State-value function -- Action-value function -- Policy -- Markov Decision Process -- Planning with dynamic programming -- Monte Carlo learning and temporal difference learning -- SARSA and Q-learning -- Deep reinforcement learning -- Practical applications of reinforcement and deep reinforcement learning algorithms -- Summary -- Chapter 3: Getting Started with OpenAI Gym and Deep Reinforcement Learning -- Code repository, setup, and configuration -- Prerequisites -- Creating the conda environment -- Minimal install - the quick and easy way -- Complete install of OpenAI Gym learning environments -- Instructions for Ubuntu -- Instructions for macOS -- MuJoCo installation -- Completing the OpenAI Gym setup -- Installing tools and libraries needed for deep reinforcement learning -- Installing prerequisite system packages -- Installing Compute Unified Device Architecture (CUDA) -- Installing PyTorch -- Summary…”

Tabla de Contenidos: “…-- Lattice Method -- Monte Carlo Method -- IS THERE A RIGHT WAY TO MODEL PREPAYMENTS? …”

Tabla de Contenidos: “…. -- Robot localization - Adaptive Monte Carlo Localization (AMCL) -- Getting ready -- How it works... -- Configuring navigation stack parameters with rqt_reconfigure -- How it works...…”

Tabla de Contenidos: “…6.7.5 - Damage Evaluation and Life Prediction -- 6.8 - Conclusions -- Chapter 7 - Inspection Interval Optimization for Aircraft Composite Structures Considering Dent Damage -- 7.1 - Introduction -- 7.2 - Damage tolerance philosophy of composite structures -- 7.2.1 - Properties of Aircraft Composite Structures -- 7.2.2 - Maintenance Model of Composite Structures -- 7.3 - Damage characterization -- 7.3.1 - Data Statistics and Category -- 7.3.2 - Damage Size Distribution -- 7.3.3 - Probability of Detection (POD) -- 7.4 - Probabilistic method -- 7.4.1 - Reliability Formulation -- 7.4.2 - Monte Carlo Simulation -- 7.5 - Case study -- 7.5.1 - Average Damages Per Life Cycle (Nd) -- 7.5.2 - Load Cases -- 7.5.3 - Damage Size and Occurrence Time -- 7.5.4 - Inspection Efficiency -- 7.5.5 - Residual Strength Reduction and Recovery -- 7.5.6 - Other Assumptions and Definitions to Facilitate the Simulation -- 7.6 - Simulation results and discussion -- 7.7 - Conclusions -- Chapter 8 - Repair Tolerance for Composite Structures Using Probabilistic Methodologies -- 8.1 - Introduction -- 8.2 - Repair tolerance -- 8.3 - Probabilistic method -- 8.4 - Case study -- 8.4.1 - Load Case -- 8.4.2 - Average Damage Per Life Cycle (Nd) -- 8.4.3 - Damage Size Distribution -- 8.4.4 - Probability of Detection (POD) -- 8.4.5 - Inspection Schedule -- 8.4.6 - Residual Strength Reduction and Recovery -- 8.4.7 - Repair Policy -- 8.4.8 - Factor of Safety -- 8.4.9 - Probability of Failure (POF) -- 8.4.10 - Maintenance Cost -- 8.5 - Results and discussion -- 8.6 - Conclusions -- Chapter 9 - Structural Health Monitoring and Influence on Current Maintenance -- 9.1 - Structural health monitoring technology -- 9.2 - SHM applications in aircraft -- 9.3 - Influence of SHM on current maintenance -- 9.4 - Integration of SHM with MSG-3 analysis -- A - Scheduled Maintenance -- B - Scheduled SHM…”

Tabla de Contenidos: “…8.2.1 - Basic specifications of generalized linear mixed models -- 8.2.2 - Statistical inference and likelihood functions -- 8.2.3 - Procedures of maximization and hypothesis testing on fixed effects -- 8.2.4 - Hypothesis testing on variance components -- 8.3 - Methods of estimating parameters in generalized linear mixed models -- 8.3.1 - Penalized quasi-likelihood method -- 8.3.2 - Marginal quasi-likelihood method -- 8.3.3 - The Laplace method -- 8.3.4 - Gaussian quadrature and adaptive Gaussian quadrature methods -- 8.3.5 - Markov Chain Monte Carlo methods -- 8.4 - Nonlinear predictions and retransformation of random components -- 8.4.1 - Best linear unbiased prediction based on linearization -- 8.4.2 - Empirical Bayes BLUP -- 8.4.3 - Retransformation method -- 8.5 - Some popular specific generalized linear mixed models -- 8.5.1 - Mixed-effects logistic regression model -- 8.5.2 - Mixed-effects ordered logistic model -- 8.5.3 - Mixed-effects multinomial logit regression models -- 8.5.4 - Mixed-effects Poisson regression models -- 8.5.5 - Survival models -- 8.6 - Summary -- Chapter 9 - Generalized estimating equations (GEEs) models -- 9.1 - Basic specifications and inferences of GEEs -- 9.1.1 - Specifications of "naïve" model with independence hypothesis -- 9.1.2 - Basic specifications of GEEs -- 9.1.3 - Specifications of working correlation matrix -- 9.1.4 - Quasi-likelihood information criteria for GEEs -- 9.2 - Other GEE approaches -- 9.2.1 - Prentice's GEE approach -- 9.2.2 - Zhao and Prentice's GEE method (GEE2) -- 9.2.3 - GEE models on odds ratios -- 9.3 - Relationship between marginal and random-effects models -- 9.3.1 - Comparison between the two approaches -- 9.3.2 - Use of GEEs to fit a conditional model -- 9.4 - Empirical illustration: effect of marital status on disability severity in older Americans -- 9.5 - Summary…”

Tabla de Contenidos: “…8.4 Modeling Interacting Oscillators of a Bacillaria Colony -- 8.4.1 Observation of the Movement Activity at Uncovered Rhaphes -- 8.4.2 Interaction of Neighboring Diatoms -- 8.4.3 Coupled Oscillators -- 8.5 Kuramoto Model -- 8.5.1 Adaptation of the Kuramoto Model for a Bacillaria Colony -- 8.5.2 Analyses and Approximations -- 8.5.3 Critical Coupling -- 8.5.3.1 Uncoupled Oscillators -- 8.5.3.2 Two Oscillators -- 8.5.3.3 Chains without Retardation -- 8.5.3.4 Identical Oscillator Frequencies and Sufficiently Small Delay -- 8.5.3.5 Remarks on the General Case -- 8.5.4 Statistical Considerations and Monte Carlo Simulations -- 8.5.4.1 Expected Value and Standard Deviation -- 8.5.4.2 Distribution of Critical Coupling -- 8.5.5 Simulation of Non-Synchronous States -- 8.5.5.1 Numerical Integration -- 8.5.5.2 Visualization of the Transient -- 8.5.5.3 Discrete Fourier Transform -- 8.5.6 Coupling to a Periodic Light Source -- 8.6 Discussion -- Acknowledgment -- References -- Chapter 9 The Psychophysical World of the Motile Diatom Bacillaria paradoxa -- Abbreviations -- 9.1 Introduction -- 9.1.1 Aneural Architecture of Bacillaria -- 9.1.2 Aneural Cognition in a Broader Context -- 9.1.3 Psychophysics as Diatom Information Processing -- 9.1.4 Information Processing and Aneural Cognition -- 9.1.5 Hebbian Intelligence and Predictive Processing -- 9.2 Measurement Techniques -- 9.2.1 Weber-Fechner Law -- 9.2.2 Connectionist Network -- 9.2.3 Algorithmic Information -- 9.2.4 Collective Pattern Generator -- 9.2.5 Dynamical States of the CoPG -- 9.3 CPGs vs. …”

Tabla de Contenidos: “…Ein Regressions-MLP mit der Sequential API erstellen -- Komplexe Modelle mit der Functional API bauen -- Dynamische Modelle mit der Subclassing API bauen -- Ein Modell sichern und wiederherstellen -- Callbacks -- TensorBoard zur Visualisierung verwenden -- Feinabstimmung der Hyperparameter eines neuronalen Netzes -- Anzahl verborgener Schichten -- Anzahl Neuronen pro verborgene Schicht -- Lernrate, Batchgröße und andere Hyperparameter -- Übungen -- Kapitel 11: Trainieren von Deep-Learning-Netzen -- Das Problem schwindender/explodierender Gradienten -- Initialisierung nach Glorot und He -- Nicht sättigende Aktivierungsfunktionen -- Batchnormalisierung -- Gradient Clipping -- Wiederverwenden vortrainierter Schichten -- Transfer Learning mit Keras -- Unüberwachtes Vortrainieren -- Vortrainieren anhand einer Hilfsaufgabe -- Schnellere Optimierer -- Momentum Optimization -- Beschleunigter Gradient nach Nesterov -- AdaGrad -- RMSProp -- Adam-Optimierung -- Scheduling der Lernrate -- Vermeiden von Overfitting durch Regularisierung -- l1- und l2-Regularisierung -- Drop-out -- Monte-Carlo-(MC-)-Drop-out -- Max-Norm-Regularisierung -- Zusammenfassung und praktische Tipps -- Übungen -- Kapitel 12: Eigene Modelle und Training mit TensorFlow -- Ein kurzer Überblick über TensorFlow -- TensorFlow wie NumPy einsetzen -- Tensoren und Operationen -- Tensoren und NumPy -- Typumwandlung -- Variablen -- Andere Datenstrukturen -- Modelle und Trainingsalgorithmen anpassen -- Eigene Verlustfunktion -- Modelle mit eigenen Komponenten sichern und laden -- Eigene Aktivierungsfunktionen, Initialisierer, Regularisierer und Constraints -- Eigene Metriken -- Eigene Schichten -- Eigene Modelle -- Verlustfunktionen und Metriken auf Modell-Interna basieren lassen -- Gradienten per Autodiff berechnen -- Eigene Trainingsschleifen -- Funktionen und Graphen in TensorFlow…”

Tabla de Contenidos: “…Cover -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Part 1: A Guide to Simulated Techniques in Digital Twin -- Chapter 1 Introduction to Different Simulation Techniques of Digital Twin Development -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Digital Twin Simulation Techniques -- 1.3.1 Finite Element Analysis Simulation -- 1.3.2 Computational Fluid Dynamics Simulation -- 1.3.3 Discrete Event Simulation -- 1.3.4 Agent-Based Modeling Simulation -- 1.3.5 Multi-Body Dynamics Simulation -- 1.3.6 Monte Carlo Simulation -- 1.4 Conclusion -- References -- Chapter 2 Comprehensive Analysis of Error Rate and Channel Capacity of Fisher Snedecor Composite Fading Model -- 2.1 Introduction -- 2.2 Fisher Snedecor Composite Fading -- 2.3 Mathematical Analysis -- 2.3.1 Error Rate Analysis -- 2.3.1.1 NCBFSK and BDPSK -- 2.3.1.2 BPSK, BFSK, and QPSK -- 2.3.1.3 MQAM -- 2.3.1.4 MPSK -- 2.3.1.5 MDPSK -- 2.3.1.6 NCMFSK -- 2.3.1.7 DQPSK -- 2.3.2 Channel Capacity Analysis -- 2.3.2.1 ORA -- 2.3.2.2 OPRA -- 2.3.2.3 CIFR -- 2.3.2.4 TIFR -- 2.4 Numerical Results -- 2.5 Conclusion -- References -- Chapter 3 Implementation of Automatic Driving Car Test Approach Based on a Digital Twinning Technology and by Embedding Artificial Intelligence -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Comparative Analysis -- 3.4 Result -- 3.5 Concluding Remarks and Future Scope -- References -- Chapter 4 Intelligent Monitoring of Transformer Equipment in Terms of Earlier Fault Diagnosis Based on Digital Twins -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Arduino Uno -- 4.2.2 ESP32 Microcontroller -- 4.2.3 Data Acquisition -- 4.2.4 Blynk App -- 4.3 Machine Learning-Based Predictive Maintenance -- 4.4 Results and Discussion -- 4.5 Conclusion and Future Work -- References…”

Tabla de Contenidos: “…Posterior Simulation: Sequential Monte Carlo -- 6.2.1. Sequential Importance Sampling and Resampling -- 6.2.2. …”

Tabla de Contenidos: “…7.5.1 Parameter Estimation for the HPP -- 7.5.2 Data Analysis for the NHPP -- 7.5.2.1 Maximum Likelihood Procedures -- 7.5.2.2 Laplace Test -- 7.6 Availability of Repairable Systems -- 7.6.1 Instantaneous (Point) Availability of Revealed-Fault Items -- 7.6.2 Instantaneous (Point) Availability of Periodically-Tested Items -- 7.6.3 Limiting Point Availability -- 7.6.4 Average Availability -- 7.6.5 Other Average Measures of Availability -- 7.6.5.1 Inherent Availability -- 7.6.5.2 Achieved Availability -- 7.6.5.3 Operational Availability -- 7.7 Markov Processes for System Availability -- 7.8 Availability of Complex Systems -- 7.9 Exercises -- References -- Chapter 8 Selected Advanced Topics in Reliability and Risk Analysis -- 8.1 Uncertainty Analysis -- 8.1.1 Steps in Uncertainty Analysis for Risk and Reliability Models -- 8.1.2 Uncertainty Propagation Methods -- 8.1.2.1 Method of Moments for Propagation of Uncertainties -- 8.1.2.2 Monte Carlo Methods for Propagation of Uncertainties -- 8.2 Analysis of Dependent Failures -- 8.2.1 Single-Parameter Models -- 8.2.2 Multiple-Parameter Models -- 8.2.3 Data Analysis for CCFs -- 8.3 Importance Measures -- 8.3.1 Birnbaum Importance -- 8.3.2 Criticality Importance -- 8.3.3 Fussell-Vesely Importance -- 8.3.4 Risk Reduction Worth Importance -- 8.3.5 Risk Achievement Worth Importance -- 8.4 Human Reliability Analysis -- 8.4.1 The HRA Process -- 8.4.2 Identifying Human Failure Events -- 8.4.2.1 THERP -- 8.4.2.2 SPAR-H -- 8.4.2.3 IDAC and Phoenix -- 8.4.2.4 IDHEAS -- 8.4.3 Representing and Quantifying HFEs -- 8.4.3.1 THERP -- 8.4.3.2 SPAR-H -- 8.4.3.3 IDAC and Phoenix -- 8.4.3.4 IDHEAS -- 8.5 Exercises -- References -- Chapter 9 Risk Analysis -- 9.1 QRA Defined -- 9.2 QRA Process -- 9.2.1 Initiating Event -- 9.2.2 Hazard Exposure Scenarios -- 9.2.3 Identification of Hazards -- 9.2.4 Identification of Barriers…”

Tabla de Contenidos: “…Carbon Footprint Tool -- 13.3 Summary -- Part III Techniques for Green Six Sigma -- 14 Quantitative Techniques -- 14.1 Introduction -- 14.2 Selection of Techniques -- 14.3 Structure of Presentation -- 14.3.1 Q1: Failure Mode and Effects Analysis -- 14.3.2 Q2: Statistical Process Control -- 14.3.3 Q3: Quality Function Deployment -- 14.3.4 Q4: Design of Experiments -- 14.3.5 Q5: Design for Six Sigma -- 14.3.6 Q6: Monte Carlo Technique -- 14.3.7 Q7: TRIZ: Inventive Problem-Solving -- 14.3.8 Q8: Measurement System Analysis -- 14.4 Summary -- 15 Qualitative Techniques -- 15.1 Introduction -- 15.2 Description of Qualitative Techniques -- 15.2.1 R1: Benchmarking -- 15.2.2 R2: The Balanced Scorecard -- 15.2.3 R3: European Foundation of Quality Management -- 15.2.4 R4: Sales and Operations Planning -- 15.2.5 R5: Knowledge Management -- 15.2.6 R6: Kanban -- 15.2.7 R7: Activity-Based Costing -- 15.2.8 R8: Quality Management Systems (ISO 9000) -- 15.2.9 R9: Kaizen -- 15.3 Summary -- Part IV Green Six Sigma and Climate Change -- 16 Climate Change Challenges -- 16.1 Introduction -- 16.2 The Earth's Temperature Is Rising -- 16.3 Greenhouse Gas Emissions by Country -- 16.4 Greenhouse Gas Emissions by Economic Sector -- 16.5 Summary -- 17 International and National Climate Change Initiatives -- 17.1 Introduction -- 17.2 International Climate Change Initiatives -- 17.2.1 The United Nations Framework Convention on Climate Change -- 17.2.2 The Kyoto Protocol -- 17.2.3 International Carbon Action Partnership -- 17.2.4 The Paris Climate Agreement -- 17.2.5 The Conference of Parties -- 17.2.6 The Intergovernmental Panel on Climate Change -- 17.2.7 Other International Initiatives -- 17.3 National Climate Change Initiatives…”

Tabla de Contenidos: “…For equity funds purchased directly: 6.6 percent -- The Merrill Lynch debacle: a case study -- Two terrible ideas: the Focus Twenty fund and the Internet Strategies fund -- A marketing success for Merrill Lynch, an investment failure for its clients -- Investment disaster: Clients lose 80 percent of their assets -- The value of financial consultants -- The rise of the robo-adviser -- Simplicity beats complexity -- The fiduciary standard -- Chapter Thirteen Profit from the Majesty of Simplicity and Parsimony: Hold Traditional Low-Cost Index Funds That Track the Stock Market -- The Monte Carlo simulation -- The majesty of simplicity in an empire of parsimony -- My conclusions rely on mathematical facts-the relentless rules of humble arithmetic -- All index funds are not created equal. …”

Tabla de Contenidos: “…. -- Step 1 - preparing the dataset -- Step 2 - computing the theoretical resolution -- Step 3 - verifying the convergence of a theoretical solution -- Step 4 - plotting the results -- Monte Carlo simulations - calibrated Hull and White short-rates -- Getting ready -- Step 1 - installing the packages and libraries -- How to do it... -- Step 2 - initializing the data and variables -- Step 3 - pricing the Bermudan swaptions -- Step 4 - constructing the spot term structure of interest rates -- Step 5 - simulating Hull-White short-rates -- Chapter 9: Structured Prediction -- Introduction -- Hidden Markov models - EUR and USD…”

Tabla de Contenidos: “…5.4 Instruction optimization -- 5.4.1 Device intrinsics -- 5.4.1.1 Directed rounding -- 5.4.1.2 C intrinsics -- 5.4.1.3 Fast math intrinsics -- 5.4.1.4 Compiler options -- 5.4.2 Divergent warps -- 6 Porting tips and techniques -- 6.1 CUF kernels -- 6.2 Conditional inclusion of code -- 6.3 Renaming variables -- 6.3.1 Renaming via use statements -- 6.3.2 Renaming via the associate construct -- 6.4 Minimizing memory footprint for work arrays -- 6.5 Array compaction -- 7 Interfacing with CUDA C code and CUDA libraries -- 7.1 Calling user-written CUDA C code -- 7.1.1 The ignore"80"137tkr directive -- 7.2 cuBLAS -- 7.2.1 Legacy cuBLAS API -- 7.2.2 New cuBLAS API -- 7.2.3 Batched cuBLAS routines -- 7.2.4 GEMM with tensor cores -- 7.3 cuSPARSE -- 7.4 cuSOLVER -- 7.5 cuTENSOR -- 7.5.1 Low-level cuTENSOR interfaces -- 7.6 Thrust -- 8 Multi-GPU programming -- 8.1 CUDA multi-GPU features -- 8.1.1 Peer-to-peer communication -- 8.1.1.1 Requirements for peer-to-peer communication -- 8.1.2 Peer-to-peer direct transfers -- 8.1.3 Peer-to-peer transpose -- 8.2 Multi-GPU programming with MPI -- 8.2.1 Assigning devices to MPI ranks -- 8.2.2 MPI transpose -- 8.2.3 GPU-aware MPI transpose -- 2 Case studies -- 9 Monte Carlo method -- 9.1 CURAND -- 9.2 Computing π with CUF kernels -- 9.2.1 IEEE-754 precision -- 9.3 Computing π with reduction kernels -- 9.3.1 Reductions with SHFL instructions -- 9.3.2 Reductions with atomic locks -- 9.3.3 Reductions using the grid"80"137group cooperative group -- 9.4 Accuracy of summation -- 9.5 Option pricing -- 10 Finite difference method -- 10.1 Nine-point 1D finite difference stencil -- 10.1.1 Data reuse and shared memory -- 10.1.2 The x-derivative kernel -- 10.1.2.1 Performance of the x-derivative kernel -- 10.1.3 Derivatives in y and z -- 10.1.4 Nonuniform grids -- 10.2 2D Laplace equation…”

Tabla de Contenidos: “…7.7 Python中用于优化的包 -- 7.8 Octave中用于优化的包 -- 7.9 Julia中用于优化的包 -- 7.10 总结 -- 7.11 回 及练习 -- 第8章 Anaconda中的无监督学习 -- 8.1 无监督学习简介 -- 8.2 层次 类 -- 8.3 k-均值 类 -- 8.4 Python包scipy简介 -- 8.5 Python包contrastive简介 -- 8.6 Python包sklearn scikit-learn 简介 -- 8.7 R包rattle简介 -- 8.8 R包randomUniformForest简介 -- 8.9 R包Rmixmod简介 -- 8.10 Julia实现 -- 8.11 类分析的任务 图 -- 8.12 总结 -- 8.13 回 及练习 -- 第9章 Anaconda中的监督学习 -- 9.1 监督学习概 -- 9.2 分类 -- 9.2.1 k-最 算法 -- 9.2.2 叶斯分类器 -- 9.2.3 强化学习 -- 9.3 监督学习的R实现 -- RtextTools简介 -- 9.4 Python实现 -- 使用scikit-learn sklearn 模块 -- 9.5 Octave实现 -- 9.6 Julia实现 -- R中机器学习的任务 图 -- 9.7 总结 -- 9.8 回 及练习 -- 第10章 数据 测分析 建模和 -- 10.1 理 数据 测分析 -- 10.2 有用的数据 -- 10.2.1 R包AppliedPredictiveModeling -- 10.2.2 时 序列分析 -- 10.3 测未来事件 -- 10.3.1 季 性 -- 10.3.2 可 化组件 -- 10.3.3 R包LiblineaR -- 10.3.4 R包datarobot -- 10.3.5 R包eclust -- 10.4 模型 择 -- 10.4.1 Python包model-catwalk -- 10.4.2 Python包sklearn -- 10.4.3 Julia包QuantEcon -- 10.4.4 Octave包ltfat -- 10.5 Granger因果关系检 -- 10.6 总结 -- 10.7 回 及练习 -- 第11章 Anaconda云 -- 11.1 Anaconda云简介 -- 11.2 深入学习Jupyter Notebook -- 11.2.1 Jupyter Notebook格式 -- 11.2.2 Notebooks分享 -- 11.2.3 目分享 -- 11.2.4 环境分享 -- 11.3 复制他人的环境到本地 -- 从Anaconda下 包 -- 11.4 总结 -- 11.5 回 及练习 -- 第12章 分布式 算、并 算和HPCC -- 12.1 分布式和并 算简介 -- 12.1.1 并 处理的任务 图 -- 12.1.2 Python示例程序 -- 12.2 理 MPI -- 12.2.1 R包Rmpi -- 12.2.2 R包plyr -- 12.2.3 R包parallel -- 12.2.4 R包snow -- 12.3 Python并 处理 -- 12.3.1 单 率的并 处理 -- 12.3.2 Monte-Carlo期权定价并 处理 -- 12.4 算 点 -- 12.5 Anaconda 加组件 -- 12.6 HPCC简介 -- 12.7 总结 -- 12.8 回 及练习…”

Tabla de Contenidos: “…7 Blockchain Technology Dislocates Traditional Practice Through Cost Cutting in International Commodity Exchange -- 7.1 Introduction -- 7.1.1 Maintenance of Documents of Supply Chain in Commodity Trading -- 7.2 Blockchain Technology -- 7.2.1 Smart Contracts -- 7.3 Blockchain Solutions -- 7.3.1 Monte Carlo Simulation in Blockchain Solution An Illustration -- 7.3.2 Supporting Blockchain Technology in the Food Industry Through Other Applications -- 7.4 Conclusion -- 7.5 Managerial Implication -- 7.6 Future Scope of Study -- References -- 8 InterPlanetary File System Protocol- Based Blockchain Framework for Routine Data and Security Management in Smart Farming -- 8.1 Introduction -- 8.1.1 Blockchain Technology for Agriculture -- 8.2 Data Management in Smart Farming -- 8.2.1 Agricultural Information -- 8.2.2 Supply Chain Efficiency -- 8.2.3 Quality Management -- 8.2.4 Nutritional Value -- 8.2.5 Food Safety -- 8.2.6 IoT Automation -- 8.3 Proposed Smart Farming Framework -- 8.3.1 Wireless Sensors -- 8.3.2 Communication Channels -- 8.3.3 IoT and Cloud Computing -- 8.3.4 Blockchain and IPFS Integration -- 8.4 Farmers Support System -- 8.4.1 Sustainable Farming -- 8.5 Results and Discussions -- 8.5.1 Benefits and Challenges -- 8.6 Conclusion -- 8.7 Future Scope -- References -- 9 A Review of Blockchain Technology -- 9.1 Introduction -- 9.1.1 Characteristics of Blockchain Technology -- 9.1.1.1 Decentralization -- 9.1.1.2 Transparency -- 9.1.1.3 Immutability -- 9.2 Related Work -- 9.3 Architecture of Blockchain and Its Components -- 9.4 Blockchain Taxonomy -- 9.4.1 Public Blockchain -- 9.4.2 Consortium Blockchain -- 9.4.3 Private Blockchain -- 9.5 Consensus Algorithms -- 9.5.1 Functions of Blockchain Consensus Mechanisms -- 9.5.2 Some Approaches to Consensus -- 9.5.2.1 Proof of Work (PoW) -- 9.5.2.2 Proof of Stake (PoS) -- 9.5.2.3 Delegated Proof of Stake (DPoS)…”

Tabla de Contenidos: “…Global forecasting event counts at interim stage -- 9.5. Monte Carlo simulation -- 9.6. Software development -- 9.6.1. …”